Recombinant Human Tissue Kallikrein-1 for Treating Acute Ischemic Stroke and Preventing Recurrence
Bibliographic record
Abstract
Novel strategies are needed for the treatment of acute ischemic stroke when revascularization therapies are not clinically appropriate or are unsuccessful. rKLK1 (recombinant human tissue kallikrein-1), a bradykinin-producing enzyme, offers a promising potential solution. In animal studies of acute stroke, there is a marked 36-fold increase in bradykinin B2 receptor on brain endothelial cells of the ischemic region. Due to this environment, rKLK1-generated bradykinin will exert a potent local vasodilation and increase brain perfusion via 3 synergistic signaling pathways downstream to the B2 receptor. Because of its preferential effect on ischemic tissue, systemic adverse effects such as hypotension are avoided with proper dosing. In addition, with initial vasodilation through recruitment of preexisting collaterals, rKLK1 promotes long-term benefit of brain perfusion by promoting new collateral formation. With an extended course of therapy for weeks after acute ischemic stroke, these multifaceted effects may also reduce the risk of stroke recurrence. A prior phase II trial demonstrated a favorable impact on clinical outcomes and recurrent strokes, particularly among patients who were not eligible for mechanical thrombectomy. A phase II/III trial has launched in this population, though opportunities for combination revascularization therapies deserve further investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".